The present disclosure relates to systems and methods for magnetic resonance imaging (“MRI”). More particularly, the present disclosure relates to systems and methods for reconstructing image from data acquired with an MRI system using a multi-level sampled data acquisition.
Sparsity-driven image reconstruction methods have shown great promise for improving spatial, temporal, and/or contrast resolution in many areas of MRI. However, due to their nonlinear nature, most sparse reconstruction methods are inherently iterative and may require the execution of many complex computational operations on large amounts of data at every iteration. Correspondingly, methods of this type remain substantially more computationally expensive than their direct, non-iterative analogs (e.g., standard SENSE) and as such have seen little translation into routine clinical practice.
MRI acquisition protocols that employ a multi-level sampling process (e.g., time-resolved CAPR) include those where the forward operator that relates the observed Fourier-domain MRI signal to the target image quantity can be factored into the product of a uniform and non-uniform sampling operator. Such multi-level sampling strategies are commonly employed for time-resolved MRI applications, where the uniform sampling operator is static and the non-uniform operator dynamically varies over time. However, existing sparse reconstruction methods typically do not leverage this property for multi-level sampled acquisitions, and operate using only the composite (i.e., forward and adjoint) sampling operators.
In light of the foregoing, there remains a need for developing sparsity-driven image reconstruction techniques that can efficiently take advantage of multi-level sampled data acquisitions.